**OutLie..R**, and kindly contributed to R-bloggers)

*Disclaimer: All prices and classes are approximate and should be confirmed at www.statistics.com as they can change. *

A comment from my previous post asked me about the experience I had in taking courses from statistics.com (www.statistics.com). To help understand how I am critiquing the courses I took, I have been teaching myself R for the past 2-3 years, and I was doing fine, but I was growing frustrated at the lack of accomplishment and growth. The straw that broke the camel’s back came when I spent 3 hours trying to figure out the mar() function in graphs.

Thankfully I looked on the R-blogger site and found an example and was able to do what I wanted. Nevertheless, I come to a crossroads of sorts and I needed some help. I had found the site before and had looked into the courses. I wanted to play it safe so I took one of the introductory courses at first to see if I liked it and then I took several others. My purpose was to improve my R skill to the point where I could be a contributing member of the R community as well as figure out code without spending an entire afternoon.

Should someone take classes from statistics.com?

If you are a beginner, or even an intermediate to advance user looking to improve on particular area or skill set….**YES**

Pros:

1. Excellent instructors- often times the instructors for these courses are the actual writers of the code, or book, or package. For example the instructor for the graphics class is Paul Murrell the author of the textbook used.

2. Good format- the 4 week format is intense but efficient. These are get in and learn fast classes, they are not going to cover all the possible topics (they just can’t), but they will cover many of the most important. For example in the statistics class we covered many of the primary tests used in statistics. Did we cover all of them…no, but we did get a flavor and understanding of the primary, which will allow students to go out and figure out the rest.

3. Good price- the price was reasonable for the course (see the website for actual pricing), about $300-$400 per class. I found this reasonable and affordable considering the instructors were such high caliber.

Side note here, I just took the R classes, I did not get a certification which requires a series of classes and are much more expensive as they are like getting an MBA then going back taking a few extra classes to get a certificate in HR. There is some confusion in that each class gives a certificate of accomplishment for each completed class, but for those taking just the R courses they are just saying you completed the course.

4. Textbooks- the textbooks were affordable when required, of the 4 course I took I only needed textbooks for two of the courses, the rest had on-line materials. For the courses with a textbook the books were ~$80 for the R Graphics (Murrell, Paul) and ~$50 for Using R for Introductory Statistics (Verzani, John). Compared to the cost of other R books and especially textbooks this is not a bad price.

5. Good examples- for the most part the courses gave excellent lessons, with good examples, where the student could accomplish the assignments, while still being challenged.

Cons:

1. Not enough time to dive into some of the more interesting topics. The balance between time and cost is always in question, and for the price there is a good balance, I just wish to have more time to ask more questions and to really understand the material better.

2. 1 week for each assignment- There were several times through the course where I wanted to have more time, or to work a head on the assignments. The problem was the assignments would show up on a Friday ad would be due 10 days later on a Sunday. I see no reason why they cannot have the due dates stay the same, while allowing all the assignments to be open from the beginning.

Overall I am impressed by the courses and would recommend them to any R programmer who wants to develop or enhance their skills.

Below is a list of various classes available for those who want with the website.

* Clinical Trials – R

* Data Mining – R

* Microarray Analysis

* R Graphics

* R Intro (data)

* R Intro (stats)

* R Modeling

* R Programming Advanced

* R Programming

* R ggplot2

* SVM in R

* Spatial Analysis in R

http://www.statistics.com/course-catalog/

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